Author name: Dr. Rania Kassir

When AI Steps In—and When It Steps Over the Line In recent months, AI chatbots like ChatGPT have surged in popularity as a source of mental health support, largely due to accessibility, affordability, and the promise of immediate responses. While these tools can offer meaningful assistance, troubling incidents have highlighted the limitations of AI and reinforced that it is not a replacement for trained mental health professionals. Real Cases That Raised Alarms Some recent events have drawn urgent attention to the risks of unsupervised AI in mental health. In one case, a 16-year-old tragically died by suicide after extensive interactions with ChatGPT. Reports suggest that the chatbot failed to direct him toward professional help and may have inadvertently reinforced harmful behavior. Similarly, a man in Connecticut allegedly committed a murder-suicide after ChatGPT appeared to amplify delusional beliefs regarding his mother. Psychiatrists have described instances of “AI psychosis,” where prolonged interaction with AI chatbots contributed to delusional or psychosis-like symptoms among vulnerable adults. These cases are stark reminders that AI, while capable of simulating empathy, lacks the nuanced understanding, ethical judgment, and crisis awareness inherent to human-led mental health care. The Benefits—and the Balance Despite these serious concerns, AI support tools can provide meaningful benefits. Chatbots can offer low-cost, immediate support for individuals experiencing mild distress or who face barriers to traditional therapy, such as financial constraints, geographic limitations, or social stigma. Trials of AI-driven tools indicate modest reductions in symptoms of depression and anxiety for mild-to-moderate cases, showing that AI can serve as a valuable adjunct rather than a replacement. Clinicians have also found AI useful for administrative and psychoeducational tasks, allowing them to dedicate more time to person-centered care. Yet, these advantages are contingent upon thoughtful use, clear boundaries, and professional oversight. Risks and Ethical Considerations AI’s limitations are clear. Emotional overattachment to chatbots may reinforce harmful beliefs, while privacy concerns and a lack of confidentiality create systemic risks. Critically, AI may mismanage crises, provide inaccurate or “hallucinated” advice, and fail to detect nonverbal cues and complex emotional signals. Without ethical safeguards, these tools can exacerbate vulnerability instead of alleviating it. Legislative action in several states has begun addressing these risks by restricting AI therapy use without licensed professional oversight. Proposed regulations emphasize the need for human supervision, accurate marketing, and clearly defined boundaries between administrative support and therapeutic guidance. Developers and AI engineers play a crucial role as well. They can design safer systems by integrating crisis detection protocols, employing human-in-the-loop review models, and avoiding anthropomorphic language that may create undue emotional dependence. Therapists, too, have a key role in guiding clients to use AI responsibly, integrating outputs as prompts for discussion rather than definitive advice, and advocating for ethical AI development aligned with clinical practice. Summary: AI as a Tool, Not a Replacement AI chatbots have potential to expand access and provide interim support, particularly for underserved populations. However, recent tragedies illustrate the risks of unsupervised use. Thoughtful regulation, clinician involvement, ethical design, and public education are essential to ensure AI supplements, rather than replaces, human therapeutic care. By using AI responsibly, we can enhance access to mental health resources while preserving the core human connection that is central to effective therapy. References

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s disease, are becoming an increasing challenge worldwide, particularly as populations age. Early detection is crucial; the sooner these conditions are identified, the greater the potential for effective intervention. Artificial intelligence (AI) is rapidly emerging as a transformative ally for clinicians—not to replace their expertise, but to enhance decision-making, efficiency, and patient-centered care. A Growing Field: AI in Neurodegenerative Research Research in AI applications for neurodegenerative disorders has grown exponentially over the past decade. A bibliometric review analyzing over 1,400 publications from 2000 to early 2025 found a significant surge in studies since 2017, driven by advances in deep learning, neural networks, and multimodal data integration. The United States and China lead in research output, while the UK produces studies with the highest citation impact (Zhang et al., 2025). This growth underscores that AI is not a distant innovation—it is actively reshaping research and clinical practice today. Early Detection: Uncovering Subtle Signals One of AI’s most promising contributions is in the early identification of neurodegenerative disorders, often before traditional clinical signs become apparent. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) has demonstrated that deep learning applied to MRI scans and other biomarkers can identify Alzheimer’s disease with more than 95% accuracy and detect mild cognitive impairment with over 82% accuracy (Alzheimer’s Disease Neuroimaging Initiative, 2025). Further, narrative reviews suggest that multi-modal and longitudinal AI models outperform single-modality approaches, offering powerful prognostic insights. While these tools are promising, integrating them into clinical practice and improving interpretability remains a critical focus for researchers (Rudroff et al., 2024). AI is also being applied in novel non-invasive ways. For instance, ophthalmic imaging powered by AI can detect retinal nerve fiber layer thinning, a biomarker for Parkinson’s disease, with diagnostic accuracy reaching an AUC of 0.918 (Tukur et al., 2025). Integrating genetic, imaging, and clinical data through AI has the potential to reshape detection and management, enabling clinicians to intervene earlier and more accurately (Mikić et al., 2025). Beyond Detection: Supporting Clinicians and Enhancing Care AI’s value extends beyond diagnostics. Administrative tasks, particularly documentation, contribute significantly to clinician burnout, reducing time for patient interaction. AI is addressing this by streamlining workflows. For example, a study led by Mass General Brigham found that ambient AI documentation systems reduced physician burnout by 21.2% while increasing documentation-related well-being by 30.7% within a few months (Mass General Brigham-led study, 2025). Similarly, AI scribes at the Permanente Medical Group saved nearly 15,800 hours of documentation in one year, allowing clinicians to focus more on patient care (Permanente Medical Group, 2025). Cleveland Clinic reported that AI reduced average documentation time by two minutes per patient visit, improving interactions without sacrificing accuracy (Cleveland Clinic, 2025). These examples highlight a central principle: AI does not replace human care but enhances it, freeing mental energy for the relational and empathetic aspects of therapy. Does AI Slow Us Down? Some experts caution that overreliance on AI might erode diagnostic skills or reduce transparency in clinical decision-making (Patel, 2025). Yet, neuroscience offers a useful analogy: as the brain adapts to disease, it reorganizes into fewer but more efficient neural networks. AI functions similarly by handling repetitive tasks, allowing clinicians to conserve cognitive resources for critical reasoning, empathy, and therapeutic connection. Importantly, oversight by trained professionals ensures AI serves as a tool rather than a replacement. Integrating AI Thoughtfully and Ethically For AI to fulfill its promise responsibly, certain standards must be maintained. Tools should be validated across diverse patient populations to ensure fairness and generalizability (Zhang et al., 2025). Clinicians must be involved in tool development and receive training to interpret AI outputs accurately (Rudroff et al., 2024). Additionally, protecting patient privacy, mitigating bias, and maintaining clinician autonomy are essential to foster trust and ethical integration. When these safeguards are in place, AI becomes an amplifier of human expertise rather than a substitute, supporting clinicians to deliver more precise, efficient, and compassionate care. Conclusion AI is increasingly shaping the landscape of neurodegenerative care—from early detection and predictive modeling to reducing administrative burdens. Its goal is not to replace clinicians but to empower them to detect disease earlier, work more efficiently, and maintain a human-centered approach to care. By thoughtfully integrating AI into clinical practice, we can preserve the most important aspect of therapy: the connection between clinician and patient. References Alzheimer’s Disease Neuroimaging Initiative. (2025). Diagnosis and prediction of Alzheimer’s from neuroimaging using deep learning. Wikipedia. https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative Cleveland Clinic. (2025, August). Less typing, more talking: AI reshapes clinical workflow at Cleveland Clinic. Cleveland Clinic Consult QD. https://consultqd.clevelandclinic.org/less-typing-more-talking-how-ambient-ai-is-reshaping-clinical-workflow-at-cleveland-clinic Mass General Brigham-led study. (2025, August 21). Ambient documentation technologies reduce physician burnout and restore ‘joy’ in medicine. Mass General Brigham Press Release. https://www.massgeneralbrigham.org/…burnout Mikić, M., et al. (2025). Public hesitancy for AI-based detection of neurodegenerative disorders. Scientific Reports. https://www.nature.com/articles/s41598-025-11917-8 Patel, A. (2025). The case for slowing down clinical AI deployment. Chief Healthcare Executive. https://www.chiefhealthcareexecutive.com/…deployment-viewpoint Permanente Medical Group. (2025, June). AI scribes save 15,000 hours—and restore the human side of medicine. AMA News Wire. https://www.ama-assn.org/…medicine Rudroff, T., Rainio, O., & Klén, R. (2024). AI for the prediction of early stages of Alzheimer’s disease from neuroimaging biomarkers—A narrative review of a growing field. arXiv. https://arxiv.org/abs/2406.17822 Tukur, H. N., et al. (2025). AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases. International Journal of Emergency Medicine, 18, Article 90. https://intjem.biomedcentral.com/articles/10.1186/s12245-025-00870-y Zhang, Y., Yu, L., Lv, Y., Yang, T., & Guo, Q. (2025). Artificial intelligence in neurodegenerative diseases research: A bibliometric analysis since 2000. Frontiers in Neurology. https://doi.org/10.3389/fneur.2025.1607924

ChatGPT has evolved significantly in recent months. From a powerful language assistant, it’s now becoming a fully integrated tool for productivity and clinical support. The latest releases—including robust email link capabilities, enhanced voice interactions, and improved reasoning models—offer real benefits to professionals in speech-language pathology (SLP), occupational therapy (OT), and psychology. 1. Email Linking & Productivity Plugins One of the most impactful updates is the ability to link ChatGPT with your email and other applications using tools like Zapier and new productivity integrations. Through ChatGPT’s email plugins, you can: This streamlines administrative workflows—an important gain for SLPs, OTs, and psychologists who often juggle paperwork, session planning, and caregiver communication. According to Sendboard, tools like Zapier plug-ins enable automation directly within ChatGPT, without requiring coding skills 2. Advanced Reasoning with o3‑pro Released in June 2025, o3‑pro enhances ChatGPT’s reasoning power. It’s stronger than earlier models and can handle complex multi-step tasks such as generating therapy plans, interpreting assessment data, or crafting detailed psychoeducational handouts For therapists, that means greater reliability and precision when using ChatGPT for: While responses may take slightly longer, the improved reliability in domains like science, coding, and detailed planning is well worth it 3. Enhanced Voice Mode Recent updates to Advanced Voice Mode have made ChatGPT’s spoken responses more natural and expressive—complete with nuanced intonation and empathetic tones. It also supports live translation between languages, expanding accessibility for non-native speakers. This opens up exciting opportunities for clinicians: These voice interactions make ChatGPT feel more conversational and human—ideal for therapeutic training, client role play, or voice modeling. 4. Smarter Projects & Context Management ChatGPT’s Projects feature, updated in June, now supports “deep research,” file uploads, and conversation sharing This allows clinicians to maintain context over long-term work—such as ongoing case notes, client resources, and clinical research—in a single workspace. For therapists: Overall, this supports richer case continuity and collaborative workflows. How Therapists Are Using ChatGPT in Smarter Ways Beyond emails and worksheets, ChatGPT’s newest updates—like memory, Projects, and advanced voice—unlock more dynamic clinical uses. Here are 5 smart ways SLPs, OTs, and psychologists are using AI in their practice: 1. Realistic Simulation for SupervisionUse ChatGPT to role-play challenging scenarios—like a child resisting transitions or a parent coping with difficult news. Great for training interns in safe, reflective ways. 2. Culturally Adapted Therapy ContentQuickly adapt stories, scripts, or visuals to reflect your client’s culture, language, or religion—boosting engagement and inclusion, especially for multilingual or neurodivergent clients. 3. Internal Script CoachingHelp clients build better self-talk for anxiety, transitions, or emotional regulation. Save and reuse personalized coping scripts across sessions for reinforcement. 4. Environment & Behavior Pattern AnalysisUpload photos or logs and ask ChatGPT to flag sensory challenges, behavior triggers, or inconsistencies across therapy settings—combining your insights with scalable pattern support.5. Assistive Tech Guides for FamiliesBreak down AAC options or executive function tools into family-friendly, visual guides. This empowers parents and teens to make informed, confident choices

Artificial intelligence (AI) is dominating social media platforms like Instagram, where rapid-fire reels, demos, and viral posts showcase exciting new AI-powered tools and breakthroughs daily. For therapists—speech-language pathologists (SLPs), occupational therapists (OTs), and psychologists—these trends offer a glimpse into the future of clinical practice. But with the constant flood of AI hype, how do you separate fleeting fads from game-changing innovations? More importantly, how can you leverage these latest AI trends to enhance your therapeutic impact ethically and effectively? This article unpacks the most viral AI updates currently buzzing on Instagram, translating them into practical insights for therapy professionals. 1. AI-Powered Video and Movement Analysis: Next-Level Remote Assessment One of the most exciting AI trends gaining traction on Instagram is real-time video analysis tools that track facial expressions, speech articulation, and fine motor movements. These tools use machine learning to provide detailed, objective data on client performance, even from recorded or live video sessions. For SLPs and OTs, this means more precise assessment and progress monitoring without needing in-person observation every time. Imagine AI algorithms flagging subtle changes in a client’s articulation or motor coordination patterns that might otherwise go unnoticed. While these platforms are still emerging and need clinical validation, they represent a promising adjunct for teletherapy and hybrid models—especially valuable given the increase in remote service delivery post-pandemic. 2. Chatbots and Virtual Assistants That Understand Emotion and Context Instagram reels are buzzing with AI chatbots demonstrating near-human conversational flow and emotional sensitivity. Advanced natural language processing (NLP) models now recognize not just words but the tone and emotional intent behind them. For psychologists and SLPs working on social skills, motivation, or emotional regulation, these AI-driven conversational agents can serve as engaging, low-stakes practice partners outside sessions. They offer clients opportunities to rehearse challenging conversations or receive immediate, non-judgmental feedback on communication attempts. However, it’s critical to view these tools as supplementary aids rather than replacements for human connection and clinical judgment. 3. Creative Content Generation: Saving Time, Boosting Engagement Social media trends highlight AI tools that instantly generate text, visuals, and even audio content tailored to user needs. Therapists can harness this to create culturally adapted stories, visual schedules, relaxation scripts, or interactive worksheets in minutes. For example, AI can transform a generic narrative retell task into a culturally relevant story for diverse client populations or produce calming mindfulness scripts aligned with a client’s language and spiritual background. By automating these time-consuming tasks, therapists free up more time to focus on individualized clinical work and relationship building. 4. Ethical Considerations: What Social Media Often Misses While Instagram showcases the shiny potential of AI, it rarely highlights the ethical complexities. Many trending AI tools operate with limited transparency on data privacy, algorithmic bias, and inclusivity. Therapists must critically evaluate new AI products by asking: Navigating these concerns ensures AI enhances care without inadvertently perpetuating inequities or privacy violations. 5. Staying Updated Without Overwhelm: Curate Your AI Learning With AI news flooding social media daily, it’s easy to feel overwhelmed or distracted by hype. To stay informed and clinically relevant: This intentional approach helps therapists keep pace with AI innovation without burnout or misinformation. Conlusion Instagram’s fast-moving AI trends reveal a dynamic future where therapy and technology increasingly intersect. By understanding and critically engaging with these viral updates, SLPs, OTs, and psychologists can adopt AI tools that truly enhance client care—balancing innovation with ethics and empathy. As AI evolves, therapists who blend technical curiosity with clinical wisdom will be best positioned to harness its full potential for meaningful, inclusive, and effective therapy.

The EU Artificial Intelligence Act (Regulation 2024/1689), passed in June 2024, is already reshaping how AI is used in therapy. While the legislation outlines clear rules for transparency, risk classification, data governance, and human oversight, the real test lies in how clinics across Europe are putting these principles into action. Clinicians—especially those working in neurodevelopmental, rehabilitative, and mental health services—are asking a vital question: How do we comply with new AI regulations while preserving the human-centered nature of therapy? Translating Regulation Into Clinical Reality Across Europe, clinics are moving from high-level compliance to day-to-day operational changes in how AI tools are selected, monitored, and disclosed. In Belgium, for instance, a pediatric neurorehabilitation center has adopted a formal internal review process. Before any AI-assisted tool is used with children, teams assess the system’s training data, analyze its outcomes across diverse populations, and require therapists to demonstrate understanding of the AI model’s functionality and limits. These steps go beyond mere legal checklists. Under the AI Act, many digital therapy tools—including those used for speech analysis, attention monitoring, or adaptive content delivery—fall into the “high-risk” category. This classification requires clinics to apply standards of robustness, explainability, and human oversight (European Union, 2024). As a result, some clinics now treat AI tools like they would Class II medical devices: requiring structured evaluation, documentation, and clinician sign-off before use. Training the Clinician, Not Just the Tool In Denmark, a national therapy center network has launched mandatory AI ethics workshops. These don’t aim to turn therapists into data scientists but to equip them with foundational AI literacy. Therapists learn to ask critical questions: This emphasis on reflective practice aligns with WHO recommendations (2024), which stress that clinicians—not algorithms—must remain the final decision-makers. AI can suggest, but it cannot interpret. A fluency tracker may flag increased pause time, but it’s the therapist who determines whether the change reflects anxiety, illness, or simply a noisy environment. Training also now includes simulated case studies. For instance, therapists might explore how two similar speech samples receive different AI scores and must trace the model’s reasoning—a process that builds their confidence in evaluating AI reliability and limitations (Schubert et al., 2025). Embedding Transparency Into Client Care Clinics in the Netherlands, France, and Germany are leading on transparency. Informed consent now includes plain-language disclosures when AI tools are involved. Families are told if AI contributes to scoring, tailoring interventions, or flagging areas of concern. This kind of transparency, especially in pediatric and disability services, builds trust and satisfies Article 52 of the Act: patients have the right to know when AI is influencing care (European Union, 2024). Some platforms are going further: In the Netherlands, a widely used SLP support app now includes pop-up explanations showing how progress scores are generated and interpreted. This allows families to discuss uncertainties with therapists and contribute to decisions, rather than passively accepting algorithmic output. Addressing Access and Digital Equity While AI tools can optimize therapy, they may also widen the digital divide. In response, clinics in Poland, Slovakia, and Hungary are piloting hybrid care models—combining traditional therapy with AI-supported modules that require minimal hardware or bandwidth. These systems use offline-first design, printable practice modules, or text-based feedback to serve rural or low-resource areas. Furthermore, multilingual and cross-cultural validity is becoming a focus. As Pérez and Cheung (2025) point out, many existing AI tools have poor generalization beyond English or neurotypical data. SLPs and OTs across Europe are beginning to collaborate with developers to improve training datasets, ensuring tools work equitably across languages, dialects, and developmental profiles. Accountability and Oversight in Action Traceability—a core principle of the EU AI Act—is being operationalized via updated clinical documentation. In a multidisciplinary clinic in Munich, therapists now log every AI-assisted decision, whether in assessment, goal-setting, or therapy delivery. This includes: These records serve not only legal protection but also longitudinal quality review. For instance, if an AI tool routinely flags phonological issues in bilingual children where clinicians find none, the system may require retraining or discontinuation. As Topol (2024) emphasizes, human oversight is not a safeguard—it’s a necessity. Emerging Lessons from Early Implementation From these clinic-led efforts, several themes are emerging: AI as a Partner, Not a Replacement The EU AI Act is more than a regulatory hurdle—it’s a catalyst for ethical, inclusive innovation. By mandating transparency, clinician oversight, and data accountability, it challenges the therapy field to move slowly and wisely, even amid rapid technological change. European clinicians are not just adapting to AI—they are shaping it. By speaking up about equity gaps, demanding better training data, and insisting on tools that reflect clinical nuance, therapists are reclaiming their role as co-creators, not passive users. The future of AI in therapy will not be about automation—it will be about augmentation, grounded in clinical judgment and compassionate care. Coming Next How clinics are designing therapist-led systems to evaluate and audit AI tools—without overwhelming paperwork or technical complexity. To find out more join our AI webinars for therapists! go to Courses to find out more details! References

AI in Clinical Care—Already Here, Already Changing Practice Artificial Intelligence is no longer theoretical. It’s embedded in our therapy rooms, electronic records, and clinical tools. From speech-language pathology to neuropsychology, AI is reshaping how we assess, document, and intervene. The question is no longer whether therapists will use AI—but how they’re doing so already, and how to do it responsibly without compromising therapeutic presence or judgment. As highlighted in our previous issue, AI is influencing not only our tools, but our decisions. Many clinicians now use AI-supported platforms—sometimes unknowingly—raising important questions about transparency, ethics, and outcomes. Schubert et al. (2025) remind us that moving from passive use to informed application requires structured education. Knowing how AI works isn’t enough; we need to understand how to reason with it, critique it, and lead its ethical use. Where AI Is Already Supporting Therapy In real-world practice, AI is already making a difference: Jha and Topol (2024) note that such tools are improving efficiency in fields reliant on pattern recognition. AI can surface meaningful shifts, propose next steps, and adapt tasks in real time. But it cannot make clinical decisions. An algorithm may detect a phonological pattern or attentional lapse. Yet the therapist must decide: Is this clinically relevant? Is it consistent with the client’s goals, history, or needs? AI can suggest. The therapist must interpret. Why Clinical Judgment Still Leads AI handles large data sets—but it cannot read between the lines. It can’t recognize when a data point reflects fatigue, emotional strain, cultural difference, or an artifact. Clinical work is not just about data. It is about human context, developmental history, motivation, and values. AI cannot weigh competing goals or make value-based decisions. As Schubert and colleagues (2025) propose, responsible AI use develops across three levels: This framework positions clinicians as decision-makers—not passive tool users, but ethical leaders. Personalization Without Losing the Personal AI can make therapy more adaptive. Some apps modify pacing or feedback in real time—slowing exercises during stuttering episodes, or increasing visual prompts for distracted learners. These features enhance responsiveness. But adaptation alone isn’t therapy. It becomes meaningful only when interpreted and guided by a clinician. Therapists remain essential in deciding whether a pattern is significant, whether a tool supports or distracts from the goal, and how to communicate these decisions to clients and families. As Topol (2024) states, AI should inform—not replace—clinical reasoning. Will AI Replace Therapists? Not Likely. Concerns about AI replacing clinicians are understandable—but not supported by current evidence. The World Health Organization (2024) affirms that best outcomes occur when clinicians retain authority and AI acts as a support. AI enhances—not diminishes—the role of skilled professionals. Clinicians with AI literacy are better equipped to: By engaging with AI critically and ethically, therapists remain stewards of care—not spectators to technological change. AI Engagement Doesn’t Require Coding—It Requires Questions You don’t need programming skills to use AI well. But you do need to ask critical questions: Functional AI literacy includes understanding key concepts like algorithmic bias, training data, and model reliability. It also involves separating evidence-based innovation from marketing hype. As WHO (2024) reminds us: clinicians are responsible for the tools they choose, even when the AI is invisible. Coming Next: Evaluating AI Tools Before You Use Them In our next article, we’ll explore how to critically evaluate an AI product before introducing it to clients. With new technologies entering the field monthly, we must remain discerning. We’ll cover: AI can’t replace what makes therapy powerful—but when used well, it can enhance connection, clarity, and compassion. To find out more join our AI webinars for therapists! go to Courses to find out more details! References Jha, S., & Topol, E. J. (2024). Adapting clinical practice to artificial intelligence: Opportunities, challenges, and ethical considerations. The Lancet Digital Health, 6(3), e175–e183. https://doi.org/10.1016/S2589-7500(24)00020-9Schubert, T., Oosterlinck, T., Stevens, R. D., Maxwell, P. H., & van der Schaar, M. (2025). AI education for clinicians. eClinicalMedicine, 79, 102968. https://doi.org/10.1016/j.eclinm.2024.102968World Health Organization. (2024). Ethics and governance of artificial intelligence for health: Guidance and tools. https://www.who.int/publications/i/item/9789240077925